In this paper, a vision-based method is proposed for cable installation tasks in constrained environments. The main challenge of such tasks lies in the potential interference between the cable and surrounding obstacles. Model-based approaches are not well-suited for these industrial scenarios due to variations in the physical properties of workpieces. To address this, the proposed method integrates a potential field-based tip trajectory regulation with a shape deformation servo. In the shape deformation servo, a planner is employed to determine a feasible shape curve that avoids obstacles. This step is crucial, as an infeasible reference for shape control may result in unstable behavior. The effectiveness of the proposed method is validated through experiments. Notably, this approach does not rely on prior model information, making it highly adaptable for industrial deployment.

Industrial part reorientation remains a critical challenge in automated manufacturing workflows, particularly with parallel-jaw grippers lacking the dexterity for complex manipulations. This paper presents a systematic flipping strategy for structured environments. A quasi-static force equilibrium model is developed to characterize multi-contact manipulation systems, and stability criteria are derived through wrench space analysis, enabling A*-based optimal trajectory generation within the derived stable configuration space. To ensure persistent fingertip-object contact, adaptive impedance control dynamically adjusts gripper stiffness based on real-time force thresholds, preventing unintended detachment. Experimental validation demonstrates robust performance in two representative scenarios:1) cube flipping on a compliant surface(84.3g, 90% success over 50 trials), 2) vision-free continuous pivoting of an irregular part on a rigid substrate(56g, 88% success over 50 trials). The methodology requires neither environmental modification nor expensive tactile sensing, showing promise for practical deployment in structured manufacturing systems.

In recent years, tactile sensors have become essential for robotic systems, particularly in tasks requiring high-precision interaction and manipulation. The Vision-Based Tactile Sensor (VBTS) represents a significant advancement in tactile sensing, utilizing cameras to monitor the deformation of soft materials at the sensor tip. Pressure applied to the sensor alters the light propagation path, thereby changing the image captured by the camera. By combining image processing and deep learning, VBTS provides highly accurate estimates of contact position and force, achieving micrometer-level resolution. This paper presents a novel VBTS design that leverages a light-conductive plate and a silicone membrane to enhance the sensor’s sensitivity to force perception. The soft, thin nature of the silicone membrane allows for precise detection of minimal forces, making it suitable for tasks involving highly deformable objects. Experimental results demonstrate the sensor’s capability in detecting contact areas and force distributions, which can be applied in diverse domains such as soft object assembly, medical assistance, and food processing. Moreover, the proposed VBTS outperforms traditional sensors by utilizing computationally efficient algorithms that maintain real-time performance without compromising resolution.